WSDSum: Unsupervised Extractive Summarization Based on Word Weight Fusion and Document Dynamic Comparison

Unsupervised extractive summarization aims to pinpoint representative sentences from raw text without relying on labeled summary data, capturing the overall content. Numerous prevalent research methods predominantly prioritize the significance of sentences within a document, potentially overlooking the importance of varying keywords within a sentence. Moreover, many methods confine the summarization to information present only in the current document, potentially omitting crucial details essential for comprehensive document understanding. To tackle these challenges, this paper introduces WSDSum, an algorithm rooted in word weight fusion and dynamic document comparison. This algorithm employs two distinct word weight assessment methods to gauge the significance of words in a sentence and subsequently combines their assessment outcomes to more effectively evaluate word importance within a sentence. Furthermore, this paper suggests a dynamic document comparison approach to enhance the diversity of the generated summaries by creating positive examples from intra-document sentences and contrasting them with inter-document sentence counterexamples. This is achieved by leveraging a cosine annealing strategy to facilitate dynamic temperature comparisons with other documents. Experimental evaluations on three public datasets indicate that WSDSum outperforms traditional methods.